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<p>Bioclimatic variables are essential indicators used across disciplines including ecology, geography, environmental science, and regional planning. While global datasets such as WorldClim and CHELSA provide climate information, the absence of regionally tailored datasets limits the ability to capture fine-scale climatic variability, which is critical for regional analysis. The Translate project addresses this gap by providing high-resolution (~1 km) observed and projected climate data for Ireland, enabling the generation of region-specific bioclimatic variables. The observational dataset was derived from long-term climate records using quality-controlled interpolation of national station data. An ensemble-based approach using ~200 Regional Climate Model (RCM) simulations was developed to generate percentile-based projections (10th, 50th, and 90th percentiles) across Representative Concentration Pathways (RCPs 2.6, 4.5, and 8.5) and multiple time periods. These percentiles represent a range of plausible climate futures and support uncertainty assessment. Three complementary approaches were applied: full-ensemble, period-specific, and scenario-specific outputs across RCPs and time periods. The observational and full-ensemble datasets each comprise 57 GeoTIFFs, with additional outputs provided for period- and scenario-specific analyses.</p>

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The Ireland BioClim dataset for observational and future climate projections

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月15日

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  • Parvaneh Nowbakht,
  • Paraic C. Ryan,
  • Jingyu Wang,
  • Jenny Harmon O’Driscoll,
  • Rosa Rogers,
  • Mark Stewart,
  • Enda O’Brien,
  • Basanta Samal,
  • Paul Nolan,
  • Paul Holloway

摘要

Bioclimatic variables are essential indicators used across disciplines including ecology, geography, environmental science, and regional planning. While global datasets such as WorldClim and CHELSA provide climate information, the absence of regionally tailored datasets limits the ability to capture fine-scale climatic variability, which is critical for regional analysis. The Translate project addresses this gap by providing high-resolution (~1 km) observed and projected climate data for Ireland, enabling the generation of region-specific bioclimatic variables. The observational dataset was derived from long-term climate records using quality-controlled interpolation of national station data. An ensemble-based approach using ~200 Regional Climate Model (RCM) simulations was developed to generate percentile-based projections (10th, 50th, and 90th percentiles) across Representative Concentration Pathways (RCPs 2.6, 4.5, and 8.5) and multiple time periods. These percentiles represent a range of plausible climate futures and support uncertainty assessment. Three complementary approaches were applied: full-ensemble, period-specific, and scenario-specific outputs across RCPs and time periods. The observational and full-ensemble datasets each comprise 57 GeoTIFFs, with additional outputs provided for period- and scenario-specific analyses.